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Preface; Contents; Part ITheoretical and Numerical Methodsand Tools for Optimization:Theoretical Methods and Tools; 1 Multi-objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current Challenges; 1.1 Introduction; 1.2 Basic Concepts; 1.3 Dealing with Expensive Problems; 1.3.1 Use of Problem Approximation; 1.3.2 Use of Functional Approximation; 1.3.3 Use of Evolutionary Approximation; 1.4 Other Approaches; 1.4.1 Cultural Algorithms; 1.4.2 Use of Very Small Population Sizes; 1.4.3 Use of Efficient Search Techniques; 1.5 Future Research Paths; 1.6 Conclusions
2.4.4 Genetic Algorithm Based Wavelet Neural Network (HYBWNN) [31, 32]2.5 Comparison Among Different Response Surface Algorithms; 2.5.1 Fittest Polynomial RBF Versus Hybrid Wavelet Neural Network [42]; 2.5.2 Fittest Polynomial RBF Versus Kriging; 2.5.3 Fittest Polynomial RBF Versus Hybrid Self Organizing Response Surface Method
HYBSORSM ; 2.5.4 Fittest Polynomial RBF Versus Kriging Approximation with Fittest Polynomial Radial Basis Function
KRG-FP-RBF; 2.6 Conclusions; References; 3 A Genetic Algorithm for a Sensor Device Location Problem; 3.1 Introductionaut]Daniele, Elia
3.2 Constrained Location Problem3.2.1 Preliminaries; 3.2.2 The Facility Location Game; 3.2.3 Location of Sensor Devices on a Grid; 3.3 Nash Genetic Algorithm for the Location Problem; 3.3.1 Genetic Algorithm; 3.3.2 Nash Equilibrium Game; 3.3.3 Test Cases; 3.4 Conclusions; References; 4 The Role of Artificial Neural Networks in Evolutionary Optimisation: A Review; 4.1 Introduction; 4.1.1 Evolutionary Algorithms; 4.1.2 Artificial Neural Networks ANN; 4.2 Different Use of ANNEO and EOANN; 4.2.1 The Use of EOs in ANNs: EOANN; 4.2.2 The Use of ANNs in EO: ANNEO
4.3 Some Applications Using ANNEO and EOANN4.4 Conclusions; References; 5 Reliability-Based Design Optimization with the Generalized Inverse Distribution Function; 5.1 Introduction; 5.2 Robust Optimization; 5.3 The Generalized Inverse Distribution Function Method; 5.4 A Robust Optimization Test Case; 5.5 Evaluating and Improving the Quantile Estimation; 5.6 Single and Multi-objective Reliability Optimization Tests; 5.7 Conclusions; References; Part IITheoretical and Numerical Methodsand Tools for Optimization:Numerical Methods and Tools
2.4.4 Genetic Algorithm Based Wavelet Neural Network (HYBWNN) [31, 32]2.5 Comparison Among Different Response Surface Algorithms; 2.5.1 Fittest Polynomial RBF Versus Hybrid Wavelet Neural Network [42]; 2.5.2 Fittest Polynomial RBF Versus Kriging; 2.5.3 Fittest Polynomial RBF Versus Hybrid Self Organizing Response Surface Method
HYBSORSM ; 2.5.4 Fittest Polynomial RBF Versus Kriging Approximation with Fittest Polynomial Radial Basis Function
KRG-FP-RBF; 2.6 Conclusions; References; 3 A Genetic Algorithm for a Sensor Device Location Problem; 3.1 Introductionaut]Daniele, Elia
3.2 Constrained Location Problem3.2.1 Preliminaries; 3.2.2 The Facility Location Game; 3.2.3 Location of Sensor Devices on a Grid; 3.3 Nash Genetic Algorithm for the Location Problem; 3.3.1 Genetic Algorithm; 3.3.2 Nash Equilibrium Game; 3.3.3 Test Cases; 3.4 Conclusions; References; 4 The Role of Artificial Neural Networks in Evolutionary Optimisation: A Review; 4.1 Introduction; 4.1.1 Evolutionary Algorithms; 4.1.2 Artificial Neural Networks ANN; 4.2 Different Use of ANNEO and EOANN; 4.2.1 The Use of EOs in ANNs: EOANN; 4.2.2 The Use of ANNs in EO: ANNEO
4.3 Some Applications Using ANNEO and EOANN4.4 Conclusions; References; 5 Reliability-Based Design Optimization with the Generalized Inverse Distribution Function; 5.1 Introduction; 5.2 Robust Optimization; 5.3 The Generalized Inverse Distribution Function Method; 5.4 A Robust Optimization Test Case; 5.5 Evaluating and Improving the Quantile Estimation; 5.6 Single and Multi-objective Reliability Optimization Tests; 5.7 Conclusions; References; Part IITheoretical and Numerical Methodsand Tools for Optimization:Numerical Methods and Tools